Yes — with 57.4 GB to spare

DeepSeek-R1-Distill-Qwen 32B at Q4_K_M fits your A100 80 GB entirely on the GPU at 8K context, at an estimated 67 tokens per second. There is room for its full 128K window.

Fully on GPU 8K context Q4_K_M · 18.4 GB MIT Released Jan 2025

MIT-licensed and close to the 70B distill on maths. A 24 GB card handles it at Q4.

What hardware do I need for DeepSeek-R1-Distill-Qwen 32B? →

The VRAM budget

weights 18.4 GB

Weights 18.4 GB KV cache @ 8K 2.00 GB Runtime overhead 0.6 GB Free 57.4 GB of 78.4 GB

Quantisation ladder

Quant	Weights	Total @ 8K	Max context	Tok/s	Quality	Fit
Q8_0	32.5 GB	35.1 GB	128K	38	−0.1% ppl	Long context
Q6_K	25.0 GB	27.6 GB	128K	49	−0.4% ppl	Long context
Q5_K_M	21.7 GB	24.3 GB	128K	57	−0.8% ppl	Long context
Q4_K_M	18.4 GB	21.0 GB	128K	67	−1.9% ppl	Recommended
Q3_K_M	14.9 GB	17.5 GB	128K	83	−5.4% ppl	Long context
Q2_K	12.8 GB	15.4 GB	128K	96	−15% ppl	Long context

Quality is the published perplexity delta against f16 weights. Max context assumes an f16 KV cache; q8_0 roughly doubles it.

How to run it

Ollama llama.cpp LM Studio vLLM

terminal

$ ollama pull deepseek-r1:32b
$ OLLAMA_CONTEXT_LENGTH=8192 \
    ollama run deepseek-r1:32b

The default. One binary, a model registry, an OpenAI-compatible port. More on Ollama.

01Download is 18.4 GB. Keep it on an SSD — a first load off a spinning disk takes minutes.

02Close anything else holding VRAM. A browser with hardware acceleration can sit on 1–2 GB.

03There is room to go to the model's full 128K context on this card.

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